This study performed statistical analyses to identify likely crash contributing factors for Head-
on Fatal and Serious Injury (FSI) collisions involving heavy vehicles (HVs) on the …

This paper provides a review of the literature regarding methods for constructing prediction
intervals for counting variables, with particular focus on those whose distributions are …

The freeway on-ramp merging section is often identified as a crash-prone spot due to the
high frequency of traffic conflicts. Cars and trucks have different sizes and operation …

In recent years, globally quantile-based model (eg quantile regression) and spatially
conditional mean models (eg geographically weighted regression) have been widely and …

Crash counts are non-negative integer events often analyzed using crash frequency models
such as the negative binomial (NB) distribution. However, due to their random and …

Accurate identification and analysis of traffic conflicts through surrogate safety measures
(SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict …

Analyzing crash data is a complex and labor-intensive process that requires careful
consideration of multiple interdependent modeling aspects, such as functional forms …

Estimating crash data count models poses a significant challenge which requires extensive
knowledge, experience, and meticulous hypothesis testing to capture underlying trends …

Abstract Motivated by the Coronavirus Disease (COVID-19) pandemic, which is due to the
SARS-CoV-2 virus, and the important problem of forecasting the number of daily deaths and …

Transportation safety researchers utilize crash prediction models (CPMs) to examine the
safety performance of roadway facilities. Using statistical modeling, the CPMs associate …